1. Field of the Invention
The present invention relates to an object detection apparatus, an object detecting method and a program for object detection; and more particularly, to an object detection apparatus, an object detecting method and an object detection program, for use in detecting an object from an image of the road in a traveling direction of a vehicle.
2. Description of the Related Art
In general, an object detection apparatus typically obtains and processes an image of an object in the front of a vehicle in a traveling direction thereof, while the vehicle being driven, by a camera unit mounted on the vehicle, and based on the result of the image processed, the apparatus classifies the object in the front of the vehicle as a vehicle, a pedestrian, or a road structure.
For example, in Japanese Patent Application Publication No. 2004-192080 (JP 2004-192080 A), a frame image before the vehicle is obtained by a CCD camera, and an object candidate area which may include an image of a leading vehicle traveling and the like are extracted from the image. A cutout plane is successively moved along Z-axis, which is the vehicle traveling direction to derive the overlapping area of the cutout plane with the object candidate area. A matching distance is calculated on the basis of the overlapping area and each reference image stored in a memory. The position in Z-axis of the cutout plane where the matching distance is minimum is determined, and the Z-axial position at that time is set as the optimum position for cutout. By comparing the object candidate area cut out in the optimum position with each reference image, the object candidate is determined as a vehicle or not depending on whether or not the matching distance is minimum.
However, in JP 2004-192080 A, because an object is detected based on a histogram produced on edge extraction frequencies of the frame image with respect to both horizontal and vertical directions, the calculation time required for detecting the object is prolonged, thereby restraining the object detection from being performed rapidly.
An object detection method using a classifier is described, in “Paul Viola and Michael J. Jones, Rapid Object Detection using a Boosted Cascade of Simple Features, IEEE CVPR (2001)” (Viola et al.). The classifier (referred to as “cascade of boosted classifiers” with Haar-like features characterized by luminance differences of rectangles) learns several hundreds images (having a specific size) of specific objects such as vehicles, faces and the like, wherein the images are defined as positive samples, and then learns arbitrary images having the same size, wherein the images are referred to as negative samples. After the end of the learning process conducted by the classifiers, an image is searched in a search window and is applied, as an ROI (Region of Interest) which is a partial image having the same size as that of the learned samples. A digit “1” is given if the partial image is approximated as a vehicle or a face. Otherwise a digit “0” is given. The classifiers (hereinafter often referred to as “dictionaries”) include a plurality of stages. Each time the stages advance, the classifier becomes more complex, thereby increasing the detection rate of a particular object and decreasing a false positive rate thereof.
However, in Viola et al., (1) no index for examining a search pixel skipping amount is described; (2) no index for determining the amount of change in the search window is described; (3) uncertainty in the position in which the detection object exists makes it impossible to define a search region; and (4) there is no complexity index of the dictionary required for detecting the detection object. For these reasons, Viola et el. suffers from problems such as: (1) time-consuming detection of objects; (2) inability to specify an object size in the image; and (3) a false detection is likely to occur.